Anastomosis is a necessary and critical part of all reconstructive surgery involving any luminal structure from cardiovascular to gastrointestinal (GI) surgery. Well over a million anastomoses are performed in the USA each year for visceral indications alone (gastrointestinal, urologic and gynecologic surgery). However, up to 30% of GI anastomoses are complicated by leakage, strictures, and stenosis, in part attributable to technical and technologic issues. An anastomotic complication significantly increases patient mortality from three times up to ten times, and diminishes the function and quality of life for affected patients. Although the minimally invasive surgical approach has transformed surgery with significantly reduced collateral tissue damage associated with access to operative sites, recent advances in surgical tools and vision technology have not addressed the critical factors influencing anastomotic outcome. This is evidenced by the lack of improvements in complication rates. To the contrary, the current minimally invasive surgery (MIS) or robot assisted surgery (RAS) pose additional new challenges for anastomosis stemming from visual and spatial limitations. The long-term goal of this research is to reduce complications and improve functional outcomes of anastomosis by robotically executing best anastomosis techniques. The following specific aims will enable the development of this technology and demonstrate feasibility, as a path to clinical adoption: Aim 1: Identify optimal suture placements using multispectral imaging. We will compare suture placements and anastomotic outcome between those guided by our novel algorithm for suture location optimization incorporating subsurface anatomic and physiologic information and those performed by expert surgeons in pre-clinical studies. Aim 2: Accurately track mobile and deformable soft tissue targets in an unstructured surgical environment. We will demonstrate how our innovative fused 3D tracking based on plenoptic imaging and NIR marker technology allows real-time, accurate identification and tracking of tissue targets during the task of anastomosis in contrast to current tracking methods in phantom and in-vivo studies. Aim 3: Compare supervised autonomous robotic control to manual anastomosis. We will compare the algorithm of automated suture planning controlled by supervised autonomous robotics to current standard master-slave robotic and manual laparoscopic technology in performing in-vivo anastomosis in preclinical studies. This research has the potential to significantly improve the function and outcome of anastomosis, independent of surgeon experience. Beyond anastomosis, adoption of this approach could be beneficial in all soft tissue MIS and RAS tasks requiring precision and maneuverability due to small working space, including pediatric and complex cardiac surgery.